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Order based feature description for high-resolution aerial image classification

OPTIK 2014年第24期125卷 7239-7243页

作者： Huang, Wei Wu, Lingda Wei, Yingmei Song, Hanchen China Satellite Maritime Tracking & Control Dept Jiangyin 214431 Peoples R China Acad Equipment Key Lab Beijing 101416 Peoples R China Natl Univ Def Technol Sch Informat Syst & Management Changsha 410073 Peoples R China

We propose to use two intensity order-based descriptors for classification of high-resolution aerial images. By analyzing the characteristics of aerial images, it points out that the imagery does not have an absolute reference frame, and can be seen as a fusion of natural image and texture image. For these characteristics, it uses two intensity order based approaches to extract low-level features of aerial images. The two descriptors are inherently rotation invariant and encode complementary information about the image. It then uses a bag of visual words model to build a holistic representation of images. Experiments results on publicly available aerial scene imagery dataset show that linear combination of order based features encodes complementary information and is significantly better than SIFT. In addition, we make a consistent comparative analysis of different classification frameworks and several feature coding methods based on aerial image dataset. (C) 2014 Elsevier GmbH. All rights reserved.

关键词： Bag of visual words (BoVW) Local invariant features Feature description aerial image classification

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Deformable patch-based-multi-layer perceptron Mixer model for forest fire aerial image classification

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JOURNAL OF APPLIED REMOTE SENSING 2023年第2期17卷

作者： Mittal, Payal Sharma, Akashdeep Singh, Raman Univ Inst Engn & Technol Comp Sci & Engn Dept Chandigarh India Thapar Univ Comp Sci & Engn Dept Patiala India Univ West Scotland Cyber Secur Dept Paisley Lans Scotland

Unmanned aerial vehicles (UAVs) that include mounted camera sensors enable a wide range of remote sensing application deployments. Due to UAVs' capacity to explore distant locations such as woods, situational awareness for applications, such as search and rescue in wildfires, estimation of endangered flora and fauna, and emergency responses have undergone a paradigm change. A multi- layer perceptron (MLP)-Mixer architecture is suggested for classifying burned piles in dense forests. Convolutional neural networks (CNNs) and the more recent attention-based transformer models have produced cutting-edge outcomes in picture prediction. Convolutions and attention are used in the MLP Mixer architecture in an effort to overcome their drawbacks and improve performance. By including a new module of DePatch in the suggested MLP Mixer model, which separates the input images in a deformable pattern to identify forest fires at an early stage, the shallow learning of CNN layers and fixed-size patch embedding in transformers were eliminated. This suggests that the DePatch-based MLP classification model outperforms transformer approaches in terms of performance, achieving a substantial accuracy of 77.23. Our proposed classification system was evaluated on the pile photos dataset taken during a burning pile of debris in an Arizona pine forest. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/***.17.022203]

关键词： aerial image classification remote sensing transformers multi-layer perceptron Mixer computer vision deep learning

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TinyML-On-The-Fly: Real-Time Low-Power and Low-Cost MCU-Embedded On-Device Computer Vision for aerial image classification

TinyML-On-The-Fly: Real-Time Low-Power and Low-Cost MCU-Embe...

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IEEE Space, Aerospace and Defence Conference (SPACE)

作者： Samanta, Riya Saha, Bidyut Ghosh, Soumya K. Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798350367393;9798350367386

aerial image classification is essential to intelligent surveillance and monitoring systems. Traditional computer vision methods either uses computational offloading to high-end servers or edge devices. However, unmanned aerial vehicles (UAVs) platforms have resource and power constraints. aerial image classification is complicated and less-expensive UAVs lack processing power and cameras. Even with large-scale computing environments, methods for classifying images are difficult to apply to aerial imagery. We propose TinyaerialNet leveraging TinyML for real-time inference on a resource-constrained ESP32 CAM. The model tested on AIDER dataset, achieves 88% on-device accuracy in the micro-controller with 103.9 KB RAM and 850 milliseconds for inference.

关键词： aerial image classification Computer Vision MobileNet TinyML On-device Inference UAV

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Archimedes Optimization with Deep Learning Based aerial image classification for Cybersecurity Enabled UAV Networks

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Computer Systems Science & Engineering 2023年第11期47卷 2171-2185页

作者： Faris Kateb Mahmoud Ragab Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The recent adoption of satellite technologies,unmanned aerial vehicles(UAVs)and 5G has encouraged telecom networking to evolve into more stable service to remote areas and render higher ***,security concerns with drones were increasing as drone nodes have been striking targets for cyberattacks because of immensely weak inbuilt and growing poor security *** study presents an Archimedes Optimization with Deep Learning based aerial image classification and Intrusion Detection(AODL-AICID)technique in secure UAV *** presented AODLAICID technique concentrates on two major processes:image classification and intrusion *** aerial image classification,the AODL-AICID technique encompasses MobileNetv2 feature extraction,Archimedes Optimization Algorithm(AOA)based hyperparameter optimizer,and backpropagation neural network(BPNN)based *** addition,the AODLAICID technique employs a stacked bi-directional long short-term memory(SBLSTM)model to accomplish intrusion detection for cybersecurity in UAV *** the final stage,the Nadam optimizer is utilized for parameter tuning of the SBLSTM *** experimental validation of the AODLAICID technique is tested and the obtained values reported the improved performance of the AODL-AICID technique over other models.

关键词： aerial image classification remote sensing intrusion detection cybersecurity deep learning

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Gabor Descriptors for aerial image classification

Gabor Descriptors for Aerial Image Classification

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10th International Conference on Artificial Neural Networks and Genetic Algorithms

作者： Risojevic, Vladimir Momic, Snjezana Babic, Zdenka Univ Banja Luke Fac Elect Engn Banja Luke Bosnia & Herceg

ISBN: (纸本)9783642202667

The amount of remote sensed imagery that has become available by far surpasses the possibility of manual analysis. One of the most important tasks in the analysis of remote sensed images is land use classification. This task can be recast as semantic classification of remote sensed images. In this paper we evaluate classifiers for semantic classification of aerial images. The evaluated classifiers are based on Gabor and Gist descriptors which have been long established in image classification tasks. We use support vector machines and propose a kernel well suited for using with Gabor descriptors. These simple classifiers achieve correct classification rate of about 90% on two datasets. From these results follows that, in aerial image classification, simple classifiers give results comparable to more complex approaches, and the pursuit for more advanced solutions should continue having this in mind.

关键词： aerial image classification Gabor filters Gist descriptor

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Generalized Category Discovery in aerial image classification via Slot Attention

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DRONES 2024年第4期8卷 160-160页

作者： Zhou, Yifan Zhu, Haoran Zhang, Yan Liang, Shuo Wang, Yujing Yang, Wen Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China CETC Res Inst 54 Shijiazhuang 050081 Peoples R China

aerial images record the dynamic Earth terrain, reflecting changes in land cover patterns caused by natural processes and human activities. Nonetheless, prevailing aerial image classification methodologies predominantly function within a closed-set framework, thereby encountering challenges when confronted with the identification of newly emerging scenes. To address this, this paper explores an aerial image recognition scenario in which a dataset comprises both labeled and unlabeled aerial images, intending to classify all images within the unlabeled subset, termed Generalized Category Discovery (GCD). It is noteworthy that the unlabeled images may pertain to labeled classes or represent novel classes. Specifically, we first develop a contrastive learning framework drawing upon the cutting-edge algorithms in GCD. Based on the multi-object characteristics of aerial images, we then propose a slot attention-based GCD training process (Slot-GCD) that contrasts learning at both the object and image levels. It decouples multiple local object features from feature maps using slots and then reconstructs the overall semantic feature of the image based on slot confidence scores and the feature map. Finally, these object-level and image-level features are input into the contrastive learning module to enable the model to learn more precise image semantic features. Comprehensive evaluations across three public aerial image datasets highlight the superiority of our approach over state-of-the-art methods. Particularly, Slot-GCD achieves a recognition accuracy of 91.5% for known old classes and 81.9% for unknown novel class data on the AID dataset.

关键词： aerial image classification generalized category discovery contrastive learning slot attention

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A Lightweight Deep Learning Model for aerial image classification and Its Application to UAV-based Disaster Management

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Procedia Computer Science 2024年 246卷 2762-2771页

作者： Deng Xinjie Burhan Khan Fazal Ghaffar Yit Hong Choo Tao Zhou Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University 75 Pigdons Road Waurn Ponds VIC 3216 Australia Chongqing Jianzhu College No.857 Lihuadadao Nanan Chongqing 400072 China

Unmanned aerial Vehicles (UAVs) play a pivotal role in disaster management and emergency response for ensuring safety of our society. In this respect, lightweight deep learning models are necessary for video and image processing onboard of UAVs for real-time applications. In this study, a useful feature extraction module known as “multi-block” is designed to effectively capture information across multiple imagery scales for facilitate lightweight deep learning models. The multi-block module incorporates depthwise convolution layers with diverse kernel sizes to ensure robust feature extraction. Then, pointwise convolution is leveraged to capture the correlation between different spatial locations and channels. Evaluated with the aerial image Database for Emergency Response (AIDER), the proposed multi-block module demonstrates a reliable performance pertaining to its feature extraction capabilities for undertaking aerial image classification tasks in disaster management.

关键词： aerial image classification Lightweight CNN Disaster management emergency response Unmanned aerial Vehicles (UAVs)

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Prairie Dog Optimization Algorithm with deep learning assisted based aerial image classification on UAV imagery

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HELIYON 2024年第18期10卷 e37446页

作者： Alkhalifa, Amal K. Saeed, Muhammad Kashif Othman, Kamal M. Ebad, Shouki A. Alonazi, Mohammed Mohamed, Abdullah Princess Nourah bint Abdulrahman Univ Appl Coll Dept Comp Sci & Informat Technol POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha Saudi Arabia Umm Al Qura Univ Coll Engn & Islamic Architecture Dept Elect Engn Mecca Saudi Arabia Northern Border Univ Fac Sci Dept Comp Sci Ar Ar 91431 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Dept Informat Syst Al Kharj 16273 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11845 Egypt

This study presents a Prairie Dog Optimization Algorithm with a Deep learning-assisted aerial image classification Approach (PDODL-AICA) on UAV images. The PDODL-AICA technique exploits the optimal DL model for classifying aerial images into numerous classes. In the presented PDODL-AICA technique, the feature extraction procedure is executed using the EfficientNetB7 model. Besides, the hyperparameter tuning of the EfficientNetB7 technique uses the PDO model. The PDODL-AICA technique uses a convolutional variational autoencoder (CVAE) model to detect and classify aerial images. The performance study of the PDODL-AICA model is implemented on a benchmark UAV image dataset. The experimental values inferred the authority of the PDODLAICA approach over recent models in terms of dissimilar measures.

关键词： aerial image classification Remote sensing UAV Prairie dog optimization Deep learning

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TinyEmergencyNet: a hardware-friendly ultra-lightweight deep learning model for aerial scene image classification

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JOURNAL OF REAL-TIME image PROCESSING 2024年第2期21卷 51-51页

作者： Mogaka, Obed M. Zewail, Rami Inoue, Koji Sayed, Mohammed S. Egypt Japan Univ Sci & Technol E JUST Dept Elect & Commun Engn New Borg El Arab City 21934 Alexandria Egypt Egypt Japan Univ Sci & Technol E JUST Dept Comp Sci & Engn New Borg El Arab City 21934 Alexandria Egypt Kyushu Univ Dept Adv Informat Technol Nishi Ku Fukuoka 8190395 Japan Zagazig Univ Dept Elect & Commun Engn Zagazig Egypt

In the context of emergency response applications, real-time situational awareness is vital. Unmanned aerial vehicles (UAVs) with imagers have emerged as crucial tools for providing timely information in such scenarios. Convolutional neural networks (CNN) are effective in image processing. However, the deployment of CNN models in UAVs faces significant challenges. The CNN models involve large number of parameters and energy-costly floating-point computations beyond the memory and power available on-board the UAVs. To address these challenges, we propose a co-design optimization approach for deploying the EmergencyNet CNN model on resource-constrained UAVs. Our strategy includes channel-wise pruning to reduce the size and optimize the network architecture. Additionally, we apply additive powers-of-two (APoT) quantization to further compress the model and enhance computational efficiency. Using channel-wise network pruning we derive TinyEmergencyNet that is only 155KB in memory size and 50% smaller than EmergencyNet. This proposed approach is evaluated on aerial image Disaster Event Recognition (AIDER) dataset. We have achieved an F1-score of 93.6% with 4-bit APoT quantization that closely approaches the full precision (32-bit) accuracy of 94%. Furthermore, hardware-friendly bit-shifting operations as a result of APoT quantization present an added advantage in hardware accelerator implementations. This work pioneers the joint application of channel-wise pruning and non-uniform APoT quantization on EmergencyNet, presenting a suitable solution tailored for UAV-based emergency response applications.

关键词： Co-design Quantization Channel-wise pruning Emergency response aerial image classification

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Evaluation of image Descriptors for Urban-Rural classification of aerial images 16

Evaluation of Image Descriptors for Urban-Rural Classificati...

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16th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT)

作者： Cortes, Daniel Nakano, Mariko Koga, Hisashi Perez, Hector Inst Politecn Nacl Mexico City DF Mexico Univ Electrocommun Beijing Peoples R China

ISBN: (纸本)9781614998006;9781614997993

In this paper, fourteen descriptors are evaluated for urban-rural classification of aerial images. Among these fourteen descriptors, eleven descriptors consist of texture-based, color-based con combination of these two descriptors. Rest three descriptors are based on dictionaries generated using the Lempel-Ziv-Welch (LZW) data compression algorithm. The classification is carried out using Support Vector Machine (SVM) with radial basis function as kernel function and KNN algorithm. The performance of these images descriptors are evaluated using accuracy, precision, sensitivity and specificity. From evaluation results, we conclude the Gabor descriptor combined with Dominant Color descriptor provides better performance, obtaining its accuracy more than 91%.

关键词： aerial image classification texture-based descriptor color-based descriptor dictionary-based descriptor LZW compression algorithm KNN SVM

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